A Fast and Simple Algorithm for computing the MLE of Amplitude Density Function Parameters
This work addresses a domain-specific need in radar telecommunication systems for improved parameter estimation, presenting an incremental method.
The paper tackles the problem of fast and accurate parameter estimation for amplitude density functions in radar applications by proposing a maximum likelihood estimator (MLE) based on projecting data onto axes, achieving efficient computation as demonstrated through simulations and real radar data analysis.
Over the last decades, the family of $α$-stale distributions has proven to be useful for modelling in telecommunication systems. Particularly, in the case of radar applications, finding a fast and accurate estimation for the amplitude density function parameters appears to be very important. In this work, the maximum likelihood estimator (MLE) is proposed for parameters of the amplitude distribution. To do this, the amplitude data are \emph{projected} on the horizontal and vertical axes using two simple transformations. It is proved that the \emph{projected} data follow a zero-location symmetric $α$-stale distribution for which the MLE can be computed quite fast. The average of computed MLEs based on two \emph{projections} is considered as estimator for parameters of the amplitude distribution. Performance of the proposed \emph{projection} method is demonstrated through simulation study and analysis of two sets of real radar data.